Dental students’ perceptions of instructor storytelling for clinical learning: A qualitative description study
Bibliographic record
Abstract
OBJECTIVE: Storytelling has been infrequently used in dental education to link clinical knowledge and practice. Our study aimed to explore dental students' views of instructor storytelling with an emphasis on clinical reasoning within a case-based oral pathology seminar. METHODS: Qualitative description guided the study design. Participants were third- and fourth-year undergraduate dental students who participated in the seminar. Data were collected through semi-structured, one-on-one interviews. Data analysis was approached using inductive, manifest thematic analysis. Verification strategies were employed to ensure methodological rigor throughout the analysis. RESULTS: In total, 21 students participated in the study ranging in age from 22 to 29 years. Three interrelated themes were identified, which were related to storytelling authenticity, benefits, and recommendations for improvement. Specifically, students reported that instructor stories effectively conveyed genuine cases and clinical reasoning; were beneficial in terms of engagement, awareness, knowledge acquisition, and skill development; and needed to be educationally and clinically relevant to bridge the knowledge-practice gap. CONCLUSIONS: Instructor storytelling was regarded by dental students as both positive and beneficial. Research is needed to further demonstrate the effectiveness of instructor storytelling in fostering clinical learning and reasoning using indirect and direct outcome measures.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".